General physicians' perspectives on SGLT2 inhibitors for heart failure.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are novel agents for heart failure (HF) and are now recommended in guidelines. Understanding general physicians' perspectives can help to optimise utilisation of this new medication.

Aim: To understand the clinical concerns and barriers from general physicians about prescribing SGLT2is in a general medicine cohort.

Physician Perceptions of Medication Prescribing in Heart Failure: A Scoping Review.

Introduction: The swift uptake of new medications into clinical practice has many benefits; however, slow uptake has been seen previously with other guideline-directed medical therapies (GDMT) in heart failure (HF). Sodium glucose co-transporter 2 inhibitors are a novel therapy in HF proven to be efficacious and will have beneficial clinical outcomes if prescribed. Understanding physician perspectives on prescribing GDMT in HF can help target strategies to bridge the gap between guidelines and practice.

Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia.

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation.

Fiber optic probe-based ATR-FTIR spectroscopy for rapid breast cancer detection: A pilot study.

Breast cancer diagnosis is crucial for timely treatment and improved outcomes. This paper proposes a novel approach for rapid breast cancer diagnosis using optical fiber probe-based attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy from 750 to 4000 cm . The technique enables direct analysis of tissue samples, eliminating the need for microtome sectioning and staining, thus saving time and resources.

Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts.

Cancer cells have an altered transcriptome, which contributes to their abnormal behavior. Many tumors have high levels of kinetochore genes, which play important roles in genome stability. This overexpression could be utilized to destabilize cancer cell genomes, however this has not been proven specifically.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.

Background: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital.

Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach.

is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently.

Overcoming resistance to EGFR monotherapy in HNSCC by identification and inhibition of individualized cancer processes.

Therapeutic strategies for advanced head and neck squamous carcinoma (HNSCC) consist of multimodal treatment, including Epidermal Growth Factor Receptor (EGFR) inhibition, immune-checkpoint inhibition, and radio (chemo) therapy. Although over 90% of HNSCC tumors overexpress EGFR, attempts to replace cytotoxic treatments with anti-EGFR agents have failed due to alternative signaling pathways and inter-tumor heterogeneity. Using protein expression data obtained from hundreds of HNSCC tissues and cell lines we compute individualized signaling signatures using an information-theoretic approach.

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures.

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed.

Explant Modeling of the Immune Environment of Head and Neck Cancer.

Patients exhibit distinct responses to immunotherapies that are thought to be linked to their tumor immune environment. However, wide variations in outcomes are also observed in patients with matched baseline tumor environments, indicating that the biological response to treatment is not currently predictable using a snapshot analysis. To investigate the relationship between the immune environment of tumors and the biological response to immunotherapies, we characterized four murine head and neck squamous cell carcinoma (HNSCC) models on two genetic backgrounds.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.

Despite huge investments and major efforts to develop remedies for Alzheimer's disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients.

Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.

The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets.

Personalized disease signatures through information-theoretic compaction of big cancer data.

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment.